Group Name : Unfiltered Commentary¶
Raees Moosa : 2322203
Oriinga Maudu : 2433303
Tumi Jourdan : 2180153
In [ ]:
import cv2
import numpy as np
import skimage
import imageio
import mpmath
import matplotlib.pyplot as plt
import seaborn as sns
import PIL
from sklearn.cluster import KMeans
from tqdm import tqdm
Question 1 Filters
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def GaussFilter(size, sigma):
values =np.arange(-(size//2), size//2 + 1)
x, y = np.meshgrid(values, values)
g = (1/(2*np.pi * sigma**2)) * np.exp(-(x**2 + y**2)/(2*sigma**2))
#normalisng it - sometimes the sum is not perfectly 1...
g /= np.sum(g)
# print(np.sum(g))
return g
test_gauss = GaussFilter(49, 10**0.5)
plt.imshow(test_gauss, cmap="inferno")
Out[ ]:
<matplotlib.image.AxesImage at 0x29c91dab350>
In [ ]:
def LoG(size, sigma):
values =np.arange(-(size//2), size//2 + 1)
x, y = np.meshgrid(values, values)
g = -((1)/(np.pi * sigma ** 4 )) * \
(1- (x**2 + y**2)/(2* sigma **2)) * \
np.exp(-(x**2 + y**2)/(2*sigma**2))
return g
test_log = LoG(49, 10**0.5)
plt.imshow(test_log,cmap="inferno")
Out[ ]:
<matplotlib.image.AxesImage at 0x29cfe7412b0>
In [ ]:
def DoG(size, sigma, K):
values =np.arange(-(size//2), size//2 + 1)
x, y = np.meshgrid(values, values)
g = ((1/(2 * np.pi * sigma **2)) * \
np.exp(-(x**2 + y ** 2)/(2 * sigma **2))) - \
((1/ (2 * np.pi * K**2 *sigma**2)) * \
np.exp(-(x**2 + y ** 2)/(2 * K**2 * sigma **2)))
return g
test_dog = DoG(49, 5, 10)
plt.imshow(test_dog,cmap="inferno")
Out[ ]:
<matplotlib.image.AxesImage at 0x29c91e274a0>
In [ ]:
# Outputs for question 1
gauss = GaussFilter(49, 10**0.5)
lgauss = LoG(49, 10**0.5)
dgauss = DoG(49,10**0.5,2)
image = cv2.imread('Images/image-35.jpg',cv2.IMREAD_GRAYSCALE)
# get gaussian
gauss = cv2.filter2D(src=image, ddepth=-1, kernel = gauss)
# get log
l_gauss = cv2.filter2D(src=image, ddepth=-1, kernel = lgauss)
# get dog
d_gauss = cv2.filter2D(src=image, ddepth=-1, kernel = dgauss)
plt.figure()
plt.subplot(1,3,1)
plt.imshow(gauss,cmap='gray')
plt.title("Gauss")
plt.subplot(1,3,2)
plt.imshow(l_gauss,cmap='gray')
plt.title("log")
plt.subplot(1,3,3)
plt.imshow(d_gauss,cmap='gray')
plt.title("dog")
Out[ ]:
Text(0.5, 1.0, 'dog')
Question 2
In [ ]:
from scipy.ndimage import convolve
def create_gaussian_filter(theta, sigma_x, sigma_y, size, filter_type='edge'):
# Create a grid of (x, y) coordinates
x = np.linspace(-size//2+1, size//2, size)
y = np.linspace(-size//2+1, size//2, size)
x, y = np.meshgrid(x, y)
# Rotate the coordinates
x_rot = x * np.cos(theta) + y * np.sin(theta)
y_rot = -x * np.sin(theta) + y * np.cos(theta)
# Calculate the Gaussian function f(xrot,sigmax)*f(yrot,sigmay)
fx = np.exp(-0.5 * (x_rot**2 / sigma_x**2))/(np.sqrt(2 * np.pi) * sigma_x)
fy = np.exp(-0.5 * (y_rot**2 / sigma_y**2))/(np.sqrt(2 * np.pi) * sigma_y)
if filter_type == 'edge':
# First derivative (edge)
#x'
dG_dx = fy*fx*(-x_rot/sigma_x**2)
#y'
dG_dy = fx*fy*(-y_rot/sigma_y**2)
return dG_dx, dG_dy
elif filter_type == 'bar':
# Second derivative (bar)
#x'
d2G_dx2 = fy*fx*((x_rot**2-sigma_x**2)/sigma_x**4)
#y'
d2G_dy2 = fx*fy*((y_rot**2-sigma_y**2)/sigma_y**4)
return d2G_dx2, d2G_dy2
else:
raise ValueError("Unknown filter type. Use 'edge' or 'bar'.")
def construct_rfs(debug: bool = False):
sigma_x_sigma_y = np.array([(3,1),(6,2),(12,4)])
thetas = np.array([0, 1/6*np.pi, 2/6*np.pi, 3/6*np.pi, 4/6*np.pi, 5/6*np.pi])
size = (49, 49)
rfs_edge = np.zeros((sigma_x_sigma_y.shape[0], thetas.shape[0], size[0], size[1]))
rfs_bar = np.zeros((sigma_x_sigma_y.shape[0], thetas.shape[0], size[0], size[1]))
for sigma_ind in range(sigma_x_sigma_y.shape[0]):
for theta_ind in range(thetas.shape[0]):
sigma = sigma_x_sigma_y[sigma_ind]
theta = thetas[theta_ind]
gaussian_edge = create_gaussian_filter(theta, sigma[0], sigma[1], size[0], 'edge')
rfs_edge[sigma_ind, theta_ind] = gaussian_edge[1]
gaussian_bar = create_gaussian_filter(theta, int(sigma[0]), sigma[1], size[0], 'bar')
rfs_bar[sigma_ind, theta_ind] = gaussian_bar[1]
# Combine rfs_edge and rfs_bar by concatenating along the theta axis
rfs_combined = np.concatenate((rfs_edge, rfs_bar), axis=0)
print(rfs_combined.shape)
def plot_filters(filters, title, size=(49, 49)):
rows, cols = filters.shape[:2]
fig, axes = plt.subplots(rows, cols, figsize=(12, 12))
fig.suptitle(title, fontsize=16)
for i in range(rows):
for j in range(cols):
ax = axes[i, j]
ax.imshow(filters[i, j], cmap='inferno')
ax.axis('off')
plt.show()
if debug:
plot_filters(rfs_combined, title="Combined Edge and Bar Filters (Y component)")
return rfs_combined
def apply_rfs_filter_scipy(image, rfs_filters):
max_responses = np.zeros((image.shape[0], image.shape[1], rfs_filters.shape[0] +2)) # plus 2 for the log and gauss
for sigma_ind in range(rfs_filters.shape[0]):
# Edge filters
responses = []
for theta_ind in range(rfs_filters.shape[1]):
filter = rfs_filters[sigma_ind, theta_ind]
response = convolve(image, filter)
responses.append(response)
max_responses[:, :, sigma_ind] = np.max(responses, axis=0)
# now apply log and gauss and add them to the responses at the end of np array
sigma = 10**0.5
log_response = convolve(image,LoG(49, sigma))
gauss_response = convolve(image,GaussFilter(49, sigma))
max_responses[:,:,max_responses.shape[2]-2] = gauss_response
max_responses[:,:,max_responses.shape[2]-1] = log_response
return max_responses
In [ ]:
mr8_image = cv2.imread("Images/image-35.jpg", cv2.IMREAD_GRAYSCALE)
# print(mr8_image.shape)
rfs_filters = construct_rfs(debug=True)
responses = []
for sigma_ind in range(rfs_filters.shape[0]):
for theta_ind in range(rfs_filters.shape[1]):
response = convolve(mr8_image,rfs_filters[sigma_ind, theta_ind])
responses.append(response)
print("count",len(responses))
rows = 6
cols = 6
fig, axes = plt.subplots(rows, cols, figsize=(12, 12))
for i in range(rows):
for j in range(cols):
ax = axes[i, j]
ax.imshow(responses[i*cols +j ],cmap='gray')
ax.axis('off')
(6, 6, 49, 49)
count 36
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mr8_image = cv2.imread("Images/image-35.jpg")
b,g,r = cv2.split(mr8_image)
b_response = apply_rfs_filter_scipy(b, rfs_filters)
g_response = apply_rfs_filter_scipy(g, rfs_filters)
r_response = apply_rfs_filter_scipy(r, rfs_filters)
In [ ]:
combined_responses = []
# Combine the responses for each filter (total of 6 filters)
for i in range(6): # Assuming each response set has shape (H, W, 6)
# Stack the R, G, B responses into a single RGB image
combined_rgb = cv2.merge((b_response[:, :, i], g_response[:, :, i], r_response[:, :, i]))
combined_responses.append(combined_rgb)
fig, axes = plt.subplots(nrows = 2, ncols= 3, figsize=(16,8), sharex= True, sharey = True)
for i in range(1,7):
plt.subplot(2,3,i)
plt.imshow(combined_responses[i-1])
plt.axis("off")
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Question 3
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def getLBPs(img,show_plot=False):
lbps = []
radii = [4, 8, 16, 24, 32]
for radius in radii:
lbp = skimage.feature.local_binary_pattern(img,12,radius,'uniform')
lbps.append(lbp)
if show_plot:
fig, axes = plt.subplots(1, 5, figsize=(15, 5))
for i, (img, label) in enumerate(zip(lbps, radii)):
axes[i].imshow(img, cmap="gray")
axes[i].axis('off')
axes[i].set_title(label)
plt.tight_layout()
plt.show()
return np.array(lbps)
In [ ]:
def apply_haar_filter(integral_images, filter_size,show_plot=False):
h, w = integral_images[0].shape
# integral images have a buffer at the end of each axis
h= h-1
w= w-1
response = np.zeros((len(integral_images),h,w))
for i in range(len(integral_images)): # For each channel (R, G, B)
integral_image = integral_images[i]
for y in range(h):
for x in range(w):
A = integral_image[max(0,y - filter_size//2),max(0,x - filter_size//2)]
B = integral_image[max(0,y - filter_size//2),min(w,x + filter_size//2)]
C = integral_image[min(h,y + filter_size//2),max(0,x - filter_size//2)]
D = integral_image[min(h,y + filter_size//2), min(w,x + filter_size//2)]
pos_sum = A + D
neg_sum = B + C
response[i,y,x] = pos_sum - neg_sum
if show_plot:
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
for i in range(response.shape[0]):
axes[i].imshow(response[i].astype(np.uint8), cmap = 'gray' )
axes[i].axis('off')
plt.tight_layout()
plt.show()
return response
In [ ]:
img = cv2.imread('Images/image-35.jpg')
red, green, blue = cv2.split(img)
red_lbps = getLBPs(red,show_plot=True)
green_lbps = getLBPs(green,show_plot=True)
blue_lbps = getLBPs(blue,show_plot=True)
print(red_lbps[0].shape)
print(green_lbps[0].shape)
print(blue_lbps[0].shape)
(450, 600) (450, 600) (450, 600)
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img = cv2.imread('Images/image-35.jpg')
integral_images =[cv2.integral(img[:, :, i]) for i in range(3)]
haar_response4 = apply_haar_filter(integral_images,4,True)
haar_response8 = apply_haar_filter(integral_images,8,True)
haar_response16 = apply_haar_filter(integral_images,16,True)
print(haar_response4.shape)
print(haar_response8.shape)
print(haar_response16.shape)
(3, 450, 600) (3, 450, 600) (3, 450, 600)
Section 4
In [ ]:
from scipy.stats import multivariate_normal
from sklearn.preprocessing import OneHotEncoder
class Stat_Classifier:
def __init__(self,image) -> None:
self.image = image
self.kmeans = None
pass
def classify(self, validation_features, fg_features, bg_features, train_mask, train_image):
# Compute the means and covariances for foreground and background
fg_feature_matrix = np.stack(fg_features, axis=-1)
fg_mean_vector = np.mean(fg_feature_matrix, axis=0)
fg_cov_matrix = np.cov(fg_feature_matrix, rowvar=False)
bg_feature_matrix = np.stack(bg_features, axis=-1)
bg_mean_vector = np.mean(bg_feature_matrix, axis=0)
bg_cov_matrix = np.cov(bg_feature_matrix, rowvar=False)
print("Features extracted from original")
# Reshape the validation features for pixel-wise processing
reshaped_features = validation_features.T
print("Reshaped the test features")
probabilities = self.foreground_given_pixel(reshaped_features, fg_mean_vector, fg_cov_matrix,
bg_mean_vector, bg_cov_matrix,mask,image)
print("Computed the feature array probabilities")
return probabilities.reshape((450,600))
def foreground_given_pixel(self,x,fg_mean, fg_cov, bg_mean, bg_cov,mask,image):
"""
Args:
mask (2d array): Remember to binarize it.
image (type):the original image.
Returns:
type: probability.
"""
N = image.shape[0]*image.shape[1]
N_fg = np.sum(mask)
N_bg = N - N_fg
numerator = multivariate_normal.pdf( x, mean = fg_mean, cov= fg_cov, allow_singular=True) * (N_fg)
denominator = multivariate_normal.pdf(x, mean=fg_mean, cov=fg_cov, allow_singular=True)*N_fg \
+ multivariate_normal.pdf( x, mean= bg_mean, cov= bg_cov, allow_singular=True) * (N_bg)
small_value = 1e-10 # You can adjust the small value if needed
denominator = np.where(denominator == 0, small_value, denominator)
probability = numerator/denominator
return probability
def getFeatures(self,training_img, mask, show_plot=False,MR8:bool = False, texton:bool = False, desired_sigma = 10**0.5,feature_matrix = [1,1,1,1,1,1]):
"""
Parameters:
training_img (2d array): training image.
mask (type): binarized image.
Returns:
type: Flattened features.
"""
if(type(mask[0][0]) != np.bool_):
binary_mask = mask >128
vertical_prewitt = np.array([
[1,1,1],
[0,0,0],
[-1,-1,-1]
])
horizontal_prewitt = np.array([
[1,0,-1],
[1,0,-1],
[1,0,-1]
])
laplacian = np.array([
[0,-1,0],
[-1,4,-1],
[0,-1,0]
])
# ceil(6*sigma) x ceil(6*sigma)
kernel_size = np.ceil(6*desired_sigma)**2
gauss = GaussFilter(kernel_size, desired_sigma)
lgauss = LoG(kernel_size, desired_sigma)
dgauss = DoG(kernel_size,desired_sigma,2)
binary_mask = mask>128
#plt.imshow(binary_mask)
#add dimensions
# print(binary_mask.shape)
hsv_training_img = cv2.cvtColor(training_img, cv2.COLOR_BGR2RGB)
v,s,h = cv2.split(hsv_training_img)
h, s,v = h*binary_mask, s*binary_mask, v*binary_mask
# print(h.shape)
b,g,r = cv2.split(training_img)
r,g,b = r*binary_mask, g*binary_mask, b*binary_mask
# get vertical prewitt for separated channels
vert_prewitt_r = cv2.filter2D(src=r, ddepth=-1, kernel=vertical_prewitt)
vert_prewitt_g = cv2.filter2D(src=g, ddepth=-1, kernel=vertical_prewitt)
vert_prewitt_b = cv2.filter2D(src=b, ddepth=-1, kernel=vertical_prewitt)
# get horizontal prewitt for separated channels
hori_prewitt_r = cv2.filter2D(src=r, ddepth=-1, kernel=horizontal_prewitt)
hori_prewitt_g = cv2.filter2D(src=g, ddepth=-1, kernel=horizontal_prewitt)
hori_prewitt_b = cv2.filter2D(src=b, ddepth=-1, kernel=horizontal_prewitt)
# get Laplacian for separated channels
laplace_r = cv2.filter2D(src=r, ddepth=-1, kernel=laplacian)
laplace_g = cv2.filter2D(src=g, ddepth=-1, kernel=laplacian)
laplace_b = cv2.filter2D(src=b, ddepth=-1, kernel=laplacian)
# get gaussian for seperate channels
gauss_r = cv2.filter2D(src=r, ddepth=-1, kernel = gauss)
gauss_g = cv2.filter2D(src=g, ddepth=-1, kernel = gauss)
gauss_b = cv2.filter2D(src=b, ddepth=-1, kernel = gauss)
# get log of gaussian for seperate channels
l_gauss_r = cv2.filter2D(src=r, ddepth=-1, kernel = lgauss)
l_gauss_g = cv2.filter2D(src=g, ddepth=-1, kernel = lgauss)
l_gauss_b = cv2.filter2D(src=b, ddepth=-1, kernel = lgauss)
# get log of gaussian for seperate channels
d_gauss_r = cv2.filter2D(src=r, ddepth=-1, kernel = dgauss)
d_gauss_g = cv2.filter2D(src=g, ddepth=-1, kernel = dgauss)
d_gauss_b = cv2.filter2D(src=b, ddepth=-1, kernel = dgauss)
# get LBPs for seperate channels
lbp_r = getLBPs(r)
lbp_g = getLBPs(g)
lbp_b = getLBPs(b)
# get Harr for seperate channels and sizes
integral_images = [cv2.integral(training_img[:,:,i]) for i in range(3)]
haar4 = apply_haar_filter(integral_images,4)
haar8 = apply_haar_filter(integral_images,8)
haar16 = apply_haar_filter(integral_images,16)
if show_plot:
# vertical prewitt plot
fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize=(16,4))
plt.subplot(1,3,1), plt.imshow( vert_prewitt_r,cmap="gray"), plt.axis("off")
plt.subplot(1,3,2), plt.imshow( vert_prewitt_g,cmap="gray"), plt.axis("off")
plt.subplot(1,3,3), plt.imshow( vert_prewitt_b,cmap="gray"), plt.axis("off")
plt.suptitle("Vertical Prewitt of RGB image")
plt.show()
# horizontal prewitt plot
fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize=(16,4))
plt.subplot(1,3,1), plt.imshow( hori_prewitt_r,cmap="gray"), plt.axis("off")
plt.subplot(1,3,2), plt.imshow( hori_prewitt_g,cmap="gray"), plt.axis("off")
plt.subplot(1,3,3), plt.imshow( hori_prewitt_b,cmap="gray"), plt.axis("off")
plt.suptitle("Horizontal Prewitt of RGB image")
plt.show()
# laplace plot
fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize=(16,4))
plt.subplot(1,3,1), plt.imshow( laplace_r,cmap="gray"), plt.axis("off")
plt.subplot(1,3,2), plt.imshow( laplace_g,cmap="gray"), plt.axis("off")
plt.subplot(1,3,3), plt.imshow( laplace_b,cmap="gray"), plt.axis("off")
plt.suptitle("Laplacian of RGB image")
plt.show()
# gaussian plot
fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize=(16,4))
plt.subplot(1,3,1), plt.imshow( gauss_r,cmap="gray"), plt.axis("off")
plt.subplot(1,3,2), plt.imshow( gauss_g,cmap="gray"), plt.axis("off")
plt.subplot(1,3,3), plt.imshow( gauss_b,cmap="gray"), plt.axis("off")
plt.suptitle("Gaussian of RGB image")
plt.show()
# log of gaussian plot
fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize=(16,4))
plt.subplot(1,3,1), plt.imshow( l_gauss_r,cmap="gray"), plt.axis("off")
plt.subplot(1,3,2), plt.imshow( l_gauss_g,cmap="gray"), plt.axis("off")
plt.subplot(1,3,3), plt.imshow( l_gauss_b,cmap="gray"), plt.axis("off")
plt.suptitle("Log of Gaussian of RGB image")
plt.show()
# difference of gaussian plot
fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize=(16,4))
plt.subplot(1,3,1), plt.imshow( d_gauss_r,cmap="gray"), plt.axis("off")
plt.subplot(1,3,2), plt.imshow( d_gauss_g,cmap="gray"), plt.axis("off")
plt.subplot(1,3,3), plt.imshow( d_gauss_b,cmap="gray"), plt.axis("off")
plt.suptitle("Difference of Gaussian of RGB image")
plt.show()
# LBP Red plot
fig, axes = plt.subplots(1, 5, figsize=(15, 5))
for i, (img, label) in enumerate(zip(lbp_r, [4,8,16,24,32])):
axes[i].imshow(img, cmap="gray")
axes[i].axis('off')
axes[i].set_title(label)
plt.suptitle("LBPs of Red image")
plt.show()
# LBP Green plot
fig, axes = plt.subplots(1, 5, figsize=(15, 5))
for i, (img, label) in enumerate(zip(lbp_g, [4,8,16,24,32])):
axes[i].imshow(img, cmap="gray")
axes[i].axis('off')
axes[i].set_title(label)
plt.suptitle("LBPs of Green image")
plt.show()
# LBP Blue plot
fig, axes = plt.subplots(1, 5, figsize=(15, 5))
for i, (img, label) in enumerate(zip(lbp_b, [4,8,16,24,32])):
axes[i].imshow(img, cmap="gray")
axes[i].axis('off')
axes[i].set_title(label)
plt.suptitle("LBPs of Blue image")
plt.show()
# Haar4 Filter plot
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
for i in range(haar4.shape[0]):
axes[i].imshow(haar4[i].astype(np.uint8),cmap="gray")
axes[i].axis('off')
plt.suptitle("Haar 4 of RGB image")
plt.show()
# Haar8 Filter plot
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
for i in range(haar8.shape[0]):
axes[i].imshow(haar8[i].astype(np.uint8),cmap="gray")
axes[i].axis('off')
plt.suptitle("Haar 8 of RGB image")
plt.show()
# Haar16 Filter plot
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
for i in range(haar16.shape[0]):
axes[i].imshow(haar16[i].astype(np.uint8),cmap="gray")
axes[i].axis('off')
plt.suptitle("Haar 16 of RGB image")
plt.show()
features = []
if (feature_matrix[0]):
features.extend([ vert_prewitt_r, hori_prewitt_r,
vert_prewitt_g, hori_prewitt_g,
vert_prewitt_b, hori_prewitt_b,
laplace_r, laplace_g, laplace_b,])
if (feature_matrix[1]):
features.extend([gauss_r, l_gauss_r, d_gauss_r,
gauss_g, l_gauss_g, d_gauss_g,
gauss_b, l_gauss_b, d_gauss_b,])
if (feature_matrix[2]):
features.extend([lbp_r[0],lbp_r[1],lbp_r[2],lbp_r[3],lbp_r[4],
lbp_g[0],lbp_g[1],lbp_g[2],lbp_g[3],lbp_g[4],
lbp_b[0],lbp_b[1],lbp_b[2],lbp_b[3],lbp_b[4],])
if (feature_matrix[3]):
features.extend([haar4[0],haar4[1],haar4[1],
haar8[0],haar8[1],haar8[1],
haar16[0],haar16[1],haar16[1],])
if (feature_matrix[4]):
features.extend([r, g, b,])
if (feature_matrix[5]):
features.extend([ h, s, v,])
if MR8:
# apply the MR8 feature bank to the HSV pixels and include these features in your model as well.
rfs_filters = construct_rfs(debug=False)
hsv = [h,s,v]
for channel in hsv:
channel_rfs_response = apply_rfs_filter_scipy(channel, rfs_filters)
for i in range(1,channel_rfs_response.shape[2]+1):
features.append(channel_rfs_response[:, :, i-1])
flattened_features = np.array([f[binary_mask].flatten() for f in features])
# print(flattened_features[0].shape)
flattened_features = np.array([f[binary_mask].flatten() for f in features])
print("Shape of flattened_features before texton:", flattened_features.shape)
if texton:
textons = self.textons(image, mask)
# One-hot encode the textons
encoder = OneHotEncoder(categories=[range(4)], sparse_output=False)
textons_one_hot = encoder.fit_transform(textons.flatten().reshape(-1, 1))
# Transpose filtered_textons to get the shape
filtered_textons = textons_one_hot.T
print("Shape of filtered_textons:", filtered_textons.shape)
# Concatenate along the features axis (features should be appended)
concatenated_features = np.concatenate([flattened_features, filtered_textons], axis=0)
print("Shape of concatenated_features:", concatenated_features.shape)
return np.array(flattened_features)
def textons(self, image, mask, plot=False):
original_features = self.getFeatures(image, mask, False)
print(original_features.shape)
perpixel_features = np.swapaxes(original_features, 0, 1)
print("Clustering!")
# mask_flattened = mask.flatten()
# masked_features = perpixel_features[mask_flattened]
kmeans = KMeans(n_clusters=4, random_state=42).fit(perpixel_features)
textons_intern = kmeans.labels_
if plot:
plt.imshow(textons_intern.reshape(mask.shape))
plt.show()
return textons_intern
def dummy_test(self, image_path):
# Mask,inverse and image (original in the lab1)
# Example usage within the dummy_test or other testing functions:
image = cv2.imread("Images/image-35.jpg")
mask = cv2.imread("Images/mask-35.png", cv2.IMREAD_GRAYSCALE)
inverse_mask = 255 - mask
class_inst = Stat_Classifier(image)
# Extract textons from the training image
# train_textons = class_inst.textons(image, )
# Validation features
null = np.ones_like(mask) * 255
validation_img = cv2.imread("Images/image-83.jpg")
validation_features = class_inst.getFeatures(validation_img, null, show_plot=False, texton=True)
fg_features = class_inst.getFeatures(image, mask, show_plot=False, texton=True)
bg_features = class_inst.getFeatures(image, inverse_mask, show_plot=False, texton=True)
# Extract textons for the validation image
# validation_textons = class_inst.textons(validation_img, null)
# Classify
verify_img = class_inst.classify(validation_features, fg_features, bg_features, mask, image)
theta = 0.5
thresholded_img = verify_img.copy() > theta
plt.figure()
plt.imshow(thresholded_img, cmap="gray"), plt.title("Validation image prediction")
plt.show()
return verify_img
# accuracy
4.2 Find and display Textons¶
In [ ]:
image = cv2.imread("Images/image-35.jpg")
mask = cv2.imread("Images/mask-35.png", cv2.IMREAD_GRAYSCALE)
classify_inst = Stat_Classifier(image)
null = np.ones_like(mask)*255
original_features = classify_inst.getFeatures(image,null,False)
print(original_features.shape)
perpixel_features = np.swapaxes(original_features,0,1)
kmeans = KMeans(n_clusters=4, random_state=42).fit(perpixel_features)
textons = kmeans.labels_
plt.imshow(textons.reshape(450,600), cmap="inferno")
Shape of flattened_features before texton: (48, 270000) (48, 270000)
Out[ ]:
<matplotlib.image.AxesImage at 0x29c8eb6b3e0>
4.3 Testing model accuracy on test Image¶
In [ ]:
test_image = cv2.imread("Images/image-83.jpg")
image = cv2.imread("Images/image-35.jpg")
print(test_image.shape)
mask = cv2.imread("Images/mask-35.png", cv2.IMREAD_GRAYSCALE)
null = np.ones_like(mask)*255
classify_inst = Stat_Classifier(image)
print("classifying the test image")
test_img_result = classify_inst.dummy_test("Images/image-83.jpg")
(450, 600, 3) classifying the test image Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Shape of flattened_features before texton: (48, 74571) Shape of flattened_features before texton: (48, 74571) (48, 74571) Clustering! Shape of filtered_textons: (4, 74571) Shape of concatenated_features: (52, 74571) Shape of flattened_features before texton: (48, 195424) Shape of flattened_features before texton: (48, 195424) (48, 195424) Clustering! Shape of filtered_textons: (4, 195424) Shape of concatenated_features: (52, 195424) Features extracted from original Reshaped the test features Computed the feature array probabilities
Accuracy using textons - IOU SCORE¶
In [ ]:
from sklearn.metrics import confusion_matrix
test_mask = cv2.imread("Images/mask-83.png", cv2.IMREAD_GRAYSCALE)
def get_IOU_PosNeg(img1,img2):
conf_matrix = confusion_matrix((img1 >0.99).astype(int).flatten(), (img2 >0.99).astype(int).flatten())
TN = conf_matrix[0][0]
fn = conf_matrix[1][0]
tp = conf_matrix[1][1]
fp = conf_matrix[0][1]
iou = tp / (tp + fp + fn)
return iou
print(get_IOU_PosNeg(test_mask, test_img_result))
0.8281889785202214
4.4 Using MR8 for classifier¶
In [ ]:
image = cv2.imread("Images/image-35.jpg")
mask = cv2.imread("Images/mask-35.png", cv2.IMREAD_GRAYSCALE)
inverse_mask = 255 - mask
class_inst = Stat_Classifier(image)
# Validation features
null = np.ones_like(mask) * 255
validation_img = cv2.imread("Images/image-83.jpg")
validation_features = class_inst.getFeatures(validation_img, null, show_plot=False,MR8= True, texton=True)
fg_features = class_inst.getFeatures(image, mask, show_plot=False,MR8= True, texton=True)
bg_features = class_inst.getFeatures(image, inverse_mask, show_plot=False,MR8= True, texton=True)
# Classify
verify_img = class_inst.classify(validation_features, fg_features, bg_features, mask, image)
theta = 0.5
thresholded_img = verify_img.copy() > theta
plt.figure()
plt.imshow(thresholded_img, cmap="gray"), plt.title("Validation image prediction")
plt.show()
(6, 6, 49, 49) Shape of flattened_features before texton: (72, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (76, 270000) (6, 6, 49, 49) Shape of flattened_features before texton: (72, 74571) Shape of flattened_features before texton: (48, 74571) (48, 74571) Clustering! Shape of filtered_textons: (4, 74571) Shape of concatenated_features: (76, 74571) (6, 6, 49, 49) Shape of flattened_features before texton: (72, 195424) Shape of flattened_features before texton: (48, 195424) (48, 195424) Clustering! Shape of filtered_textons: (4, 195424) Shape of concatenated_features: (76, 195424) Features extracted from original Reshaped the test features Computed the feature array probabilities
Accuracy of MR8 + TExtons - IOU SCORE¶
In [ ]:
test_mask = cv2.imread("Images/mask-83.png", cv2.IMREAD_GRAYSCALE)
print(get_IOU_PosNeg(test_mask, verify_img))
0.7847404063205418
Question 4.5
In [ ]:
import sklearn.linear_model
image = cv2.imread("Images/image-35.jpg")
mask = cv2.imread("Images/mask-35.png", cv2.IMREAD_GRAYSCALE)
mask = mask>=127.5
null = np.ones_like(mask)*255
classify_inst = Stat_Classifier(image)
original_features = classify_inst.getFeatures(image,null,False,MR8=True)
perpixel_features = np.swapaxes(original_features,0,1)
log_reg = sklearn.linear_model.LogisticRegression().fit(perpixel_features,mask.flatten())
(6, 6, 49, 49) Shape of flattened_features before texton: (72, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
In [ ]:
ver = cv2.imread("Images/image-110.jpg")
mask = cv2.imread("Images/mask-110.png", cv2.IMREAD_GRAYSCALE)
mask = mask>=127.5
ver_features = classify_inst.getFeatures(ver,null,False,MR8=True,texton=True)
ver_perpixel_features = np.swapaxes(ver_features,0,1)
predictions = log_reg.predict(ver_perpixel_features)
print("Accuracy", log_reg.score(ver_perpixel_features,mask.flatten()))
print(predictions.shape)
plt.imshow(predictions.reshape(450,600),cmap = 'gray')
(6, 6, 49, 49) Shape of flattened_features before texton: (72, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (76, 270000) Accuracy 0.9647222222222223 (270000,)
Out[ ]:
<matplotlib.image.AxesImage at 0x29c832a29c0>
In [ ]:
from itertools import product
from tqdm import tqdm
# Running features with and without MR8
# Length of the boolean array
n = 6
# Generate all possible combinations of 0s and 1s
combinations = list(product([0, 1], repeat=n))
# Convert tuples to lists
combinations = [list(comb) for comb in combinations]
accuracies = []
# Print the combinations
# MR8 and textons
for comb in tqdm(combinations):
if(comb != [0,0,0,0,0,0]):
import sklearn.linear_model
image = cv2.imread("Images/image-35.jpg")
mask = cv2.imread("Images/mask-35.png", cv2.IMREAD_GRAYSCALE)
mask = mask>=127.5
null = np.ones_like(mask)*255
classify_inst = Stat_Classifier(image)
original_features = classify_inst.getFeatures(image,null,False,MR8=True,feature_matrix=comb)
perpixel_features = np.swapaxes(original_features,0,1)
log_reg = sklearn.linear_model.LogisticRegression().fit(perpixel_features,mask.flatten())
ver = cv2.imread("Images/image-110.jpg")
mask = cv2.imread("Images/mask-110.png", cv2.IMREAD_GRAYSCALE)
mask = mask>=127.5
ver_features = classify_inst.getFeatures(ver,null,False,MR8=True,texton=True,feature_matrix=comb)
ver_perpixel_features = np.swapaxes(ver_features,0,1)
predictions = log_reg.predict(ver_perpixel_features)
print("Accuracy", log_reg.score(ver_perpixel_features,mask.flatten()))
accuracies.append(log_reg.score(ver_perpixel_features,mask.flatten()))
accuracies1 = accuracies
# only textons
accuracies = []
for comb in tqdm(combinations):
if(comb != [0,0,0,0,0,0]):
import sklearn.linear_model
image = cv2.imread("Images/image-35.jpg")
mask = cv2.imread("Images/mask-35.png", cv2.IMREAD_GRAYSCALE)
mask = mask>=127.5
null = np.ones_like(mask)*255
classify_inst = Stat_Classifier(image)
original_features = classify_inst.getFeatures(image,null,False,MR8=False,feature_matrix=comb)
perpixel_features = np.swapaxes(original_features,0,1)
log_reg = sklearn.linear_model.LogisticRegression().fit(perpixel_features,mask.flatten())
ver = cv2.imread("Images/image-110.jpg")
mask = cv2.imread("Images/mask-110.png", cv2.IMREAD_GRAYSCALE)
mask = mask>=127.5
ver_features = classify_inst.getFeatures(ver,null,False,MR8=False,texton=True,feature_matrix=comb)
ver_perpixel_features = np.swapaxes(ver_features,0,1)
predictions = log_reg.predict(ver_perpixel_features)
print("Accuracy", log_reg.score(ver_perpixel_features,mask.flatten()))
accuracies.append(log_reg.score(ver_perpixel_features,mask.flatten()))
accuracies2 = accuracies
print(np.argmax(accuracies1))
print(np.argmax(accuracies2))
0%| | 0/64 [00:00<?, ?it/s]
(6, 6, 49, 49) Shape of flattened_features before texton: (27, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (27, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
3%|▎ | 2/64 [01:29<46:07, 44.64s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (31, 270000) Accuracy 0.9776555555555556 (6, 6, 49, 49) Shape of flattened_features before texton: (27, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (27, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
5%|▍ | 3/64 [03:01<1:05:22, 64.30s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (31, 270000) Accuracy 0.9776851851851852 (6, 6, 49, 49) Shape of flattened_features before texton: (30, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (30, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
6%|▋ | 4/64 [04:31<1:13:58, 73.97s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (34, 270000) Accuracy 0.9769592592592593 (6, 6, 49, 49) Shape of flattened_features before texton: (33, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (33, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
8%|▊ | 5/64 [05:59<1:17:43, 79.05s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (37, 270000) Accuracy 0.9627333333333333 (6, 6, 49, 49) Shape of flattened_features before texton: (36, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (36, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
9%|▉ | 6/64 [07:26<1:18:47, 81.51s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (40, 270000) Accuracy 0.9623666666666667 (6, 6, 49, 49) Shape of flattened_features before texton: (36, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (36, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
11%|█ | 7/64 [08:53<1:19:03, 83.23s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (40, 270000) Accuracy 0.9632407407407407 (6, 6, 49, 49) Shape of flattened_features before texton: (39, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (39, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
12%|█▎ | 8/64 [10:19<1:18:37, 84.25s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (43, 270000) Accuracy 0.9612666666666667 (6, 6, 49, 49) Shape of flattened_features before texton: (39, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (39, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
14%|█▍ | 9/64 [11:46<1:17:50, 84.92s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (43, 270000) Accuracy 0.9223555555555556 (6, 6, 49, 49) Shape of flattened_features before texton: (42, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (42, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
16%|█▌ | 10/64 [13:12<1:16:44, 85.28s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (46, 270000) Accuracy 0.9366666666666666 (6, 6, 49, 49) Shape of flattened_features before texton: (42, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (42, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
17%|█▋ | 11/64 [14:38<1:15:33, 85.53s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (46, 270000) Accuracy 0.9366666666666666 (6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
19%|█▉ | 12/64 [16:05<1:14:29, 85.95s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (49, 270000) Accuracy 0.9595407407407407 (6, 6, 49, 49) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
20%|██ | 13/64 [17:32<1:13:22, 86.32s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Accuracy 0.9629074074074074 (6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (55, 270000) Accuracy 0.9618555555555556
22%|██▏ | 14/64 [19:00<1:12:17, 86.76s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
23%|██▎ | 15/64 [20:27<1:11:03, 87.00s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (55, 270000) Accuracy 0.9614962962962963 (6, 6, 49, 49) Shape of flattened_features before texton: (54, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (54, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (58, 270000) Accuracy 0.9623962962962963
25%|██▌ | 16/64 [21:54<1:09:38, 87.05s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (33, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (33, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
27%|██▋ | 17/64 [23:20<1:07:47, 86.55s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (37, 270000) Accuracy 0.9764 (6, 6, 49, 49) Shape of flattened_features before texton: (36, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (36, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
28%|██▊ | 18/64 [24:45<1:06:08, 86.27s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (40, 270000) Accuracy 0.9779814814814815 (6, 6, 49, 49) Shape of flattened_features before texton: (36, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (36, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
30%|██▉ | 19/64 [26:11<1:04:34, 86.09s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (40, 270000) Accuracy 0.9779148148148148 (6, 6, 49, 49) Shape of flattened_features before texton: (39, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (39, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
31%|███▏ | 20/64 [27:37<1:03:02, 85.97s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (43, 270000) Accuracy 0.9790925925925926 (6, 6, 49, 49) Shape of flattened_features before texton: (42, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (42, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
33%|███▎ | 21/64 [29:03<1:01:36, 85.97s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (46, 270000) Accuracy 0.9621222222222222 (6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
34%|███▍ | 22/64 [30:29<1:00:15, 86.09s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (49, 270000) Accuracy 0.9616518518518519 (6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
36%|███▌ | 23/64 [31:55<58:53, 86.18s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (49, 270000) Accuracy 0.9614962962962963 (6, 6, 49, 49) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
38%|███▊ | 24/64 [33:22<57:27, 86.20s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Accuracy 0.9619666666666666 (6, 6, 49, 49) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Accuracy 0.9539592592592593
39%|███▉ | 25/64 [34:48<56:04, 86.28s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (55, 270000) Accuracy 0.9430851851851851
41%|████ | 26/64 [36:15<54:44, 86.44s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (55, 270000) Accuracy 0.9430481481481482
42%|████▏ | 27/64 [37:42<53:22, 86.55s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (54, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (54, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (58, 270000) Accuracy 0.9661222222222222
44%|████▍ | 28/64 [39:10<52:14, 87.06s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (57, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (57, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (61, 270000) Accuracy 0.9613111111111111
45%|████▌ | 29/64 [40:38<50:54, 87.27s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (60, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (60, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (64, 270000) Accuracy 0.9617518518518519
47%|████▋ | 30/64 [42:06<49:35, 87.51s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (60, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (60, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (64, 270000) Accuracy 0.9622851851851851
48%|████▊ | 31/64 [43:34<48:12, 87.65s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (63, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (63, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (67, 270000) Accuracy 0.961237037037037
50%|█████ | 32/64 [45:02<46:46, 87.69s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (33, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (33, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
52%|█████▏ | 33/64 [46:27<44:57, 87.02s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (37, 270000) Accuracy 0.9353518518518519 (6, 6, 49, 49) Shape of flattened_features before texton: (36, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (36, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
53%|█████▎ | 34/64 [47:53<43:18, 86.61s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (40, 270000) Accuracy 0.9625888888888889 (6, 6, 49, 49) Shape of flattened_features before texton: (36, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (36, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
55%|█████▍ | 35/64 [49:18<41:41, 86.26s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (40, 270000) Accuracy 0.9625851851851852 (6, 6, 49, 49) Shape of flattened_features before texton: (39, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (39, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
56%|█████▋ | 36/64 [50:44<40:09, 86.07s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (43, 270000) Accuracy 0.9639629629629629 (6, 6, 49, 49) Shape of flattened_features before texton: (42, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (42, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
58%|█████▊ | 37/64 [52:10<38:41, 85.98s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (46, 270000) Accuracy 0.9613407407407407 (6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
59%|█████▉ | 38/64 [53:36<37:18, 86.11s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (49, 270000) Accuracy 0.9599074074074074 (6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
61%|██████ | 39/64 [55:02<35:55, 86.23s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (49, 270000) Accuracy 0.9625703703703704 (6, 6, 49, 49) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
62%|██████▎ | 40/64 [56:29<34:29, 86.24s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Accuracy 0.9624814814814815 (6, 6, 49, 49) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
64%|██████▍ | 41/64 [57:55<33:01, 86.17s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Accuracy 0.9251666666666667 (6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
66%|██████▌ | 42/64 [59:21<31:39, 86.33s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (55, 270000) Accuracy 0.9363037037037037 (6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
67%|██████▋ | 43/64 [1:00:48<30:13, 86.37s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (55, 270000) Accuracy 0.9368666666666666 (6, 6, 49, 49) Shape of flattened_features before texton: (54, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (54, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
69%|██████▉ | 44/64 [1:02:15<28:49, 86.49s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (58, 270000) Accuracy 0.9444814814814815 (6, 6, 49, 49) Shape of flattened_features before texton: (57, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (57, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (61, 270000) Accuracy 0.9615518518518519
70%|███████ | 45/64 [1:03:42<27:28, 86.77s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (60, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (60, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (64, 270000) Accuracy 0.9610222222222222
72%|███████▏ | 46/64 [1:05:10<26:06, 87.04s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (60, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (60, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (64, 270000) Accuracy 0.9614962962962963
73%|███████▎ | 47/64 [1:06:37<24:43, 87.24s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (63, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (63, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (67, 270000) Accuracy 0.9617740740740741
75%|███████▌ | 48/64 [1:08:05<23:19, 87.44s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (42, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (42, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
77%|███████▋ | 49/64 [1:09:31<21:43, 86.93s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (46, 270000) Accuracy 0.9688481481481481 (6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
78%|███████▊ | 50/64 [1:10:57<20:13, 86.67s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (49, 270000) Accuracy 0.9624444444444444 (6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (45, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
80%|███████▉ | 51/64 [1:12:23<18:43, 86.45s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (49, 270000) Accuracy 0.9628592592592593 (6, 6, 49, 49) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
81%|████████▏ | 52/64 [1:13:49<17:15, 86.32s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Accuracy 0.9776925925925926 (6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (51, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
83%|████████▎ | 53/64 [1:15:16<15:50, 86.41s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (55, 270000) Accuracy 0.9616925925925925 (6, 6, 49, 49) Shape of flattened_features before texton: (54, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (54, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
84%|████████▍ | 54/64 [1:16:43<14:25, 86.54s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (58, 270000) Accuracy 0.9614037037037036 (6, 6, 49, 49) Shape of flattened_features before texton: (54, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (54, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
86%|████████▌ | 55/64 [1:18:09<12:59, 86.60s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (58, 270000) Accuracy 0.9613481481481482 (6, 6, 49, 49) Shape of flattened_features before texton: (57, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (57, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (61, 270000) Accuracy 0.9611444444444445
88%|████████▊ | 56/64 [1:19:37<11:34, 86.80s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (57, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (57, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (61, 270000) Accuracy 0.9385222222222223
89%|████████▉ | 57/64 [1:21:04<10:08, 86.86s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (60, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (60, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
91%|█████████ | 58/64 [1:22:31<08:41, 86.99s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (64, 270000) Accuracy 0.9439296296296297 (6, 6, 49, 49) Shape of flattened_features before texton: (60, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (60, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (64, 270000) Accuracy 0.9439333333333333
92%|█████████▏| 59/64 [1:23:58<07:15, 87.13s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (63, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (63, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (67, 270000) Accuracy 0.9389148148148149
94%|█████████▍| 60/64 [1:25:26<05:48, 87.18s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (66, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (66, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (70, 270000) Accuracy 0.9619111111111112
95%|█████████▌| 61/64 [1:26:54<04:22, 87.38s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (69, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (69, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (73, 270000) Accuracy 0.9622
97%|█████████▋| 62/64 [1:28:22<02:55, 87.64s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (69, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (69, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (73, 270000) Accuracy 0.9616370370370371
98%|█████████▊| 63/64 [1:29:50<01:27, 87.74s/it]
(6, 6, 49, 49) Shape of flattened_features before texton: (72, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
(6, 6, 49, 49) Shape of flattened_features before texton: (72, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (76, 270000) Accuracy 0.9647222222222223
100%|██████████| 64/64 [1:31:19<00:00, 85.62s/it] 0%| | 0/64 [00:00<?, ?it/s]
Shape of flattened_features before texton: (3, 270000) Shape of flattened_features before texton: (3, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
3%|▎ | 2/64 [00:13<06:56, 6.71s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (7, 270000) Accuracy 0.9676888888888889 Shape of flattened_features before texton: (3, 270000) Shape of flattened_features before texton: (3, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
5%|▍ | 3/64 [00:26<09:38, 9.49s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (7, 270000) Accuracy 0.9676888888888889 Shape of flattened_features before texton: (6, 270000) Shape of flattened_features before texton: (6, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
6%|▋ | 4/64 [00:40<10:59, 10.99s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (10, 270000) Accuracy 0.9676888888888889 Shape of flattened_features before texton: (9, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (9, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
8%|▊ | 5/64 [00:54<11:54, 12.11s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (13, 270000) Accuracy 0.9653074074074074 Shape of flattened_features before texton: (12, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (12, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
9%|▉ | 6/64 [01:08<12:22, 12.79s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (16, 270000) Accuracy 0.9644 Shape of flattened_features before texton: (12, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (12, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
11%|█ | 7/64 [01:22<12:33, 13.22s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (16, 270000) Accuracy 0.9644148148148148 Shape of flattened_features before texton: (15, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (15, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
12%|█▎ | 8/64 [01:37<12:42, 13.61s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (19, 270000) Accuracy 0.9683074074074074 Shape of flattened_features before texton: (15, 270000) Shape of flattened_features before texton: (15, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
14%|█▍ | 9/64 [01:51<12:35, 13.74s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (19, 270000) Accuracy 0.776062962962963 Shape of flattened_features before texton: (18, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (18, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
16%|█▌ | 10/64 [02:05<12:33, 13.96s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (22, 270000) Accuracy 0.9808296296296296 Shape of flattened_features before texton: (18, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (18, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
17%|█▋ | 11/64 [02:20<12:28, 14.12s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (22, 270000) Accuracy 0.9769148148148148 Shape of flattened_features before texton: (21, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (21, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
19%|█▉ | 12/64 [02:34<12:22, 14.28s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (25, 270000) Accuracy 0.9835851851851852 Shape of flattened_features before texton: (24, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (24, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
20%|██ | 13/64 [02:49<12:16, 14.43s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (28, 270000) Accuracy 0.9616962962962963 Shape of flattened_features before texton: (27, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (27, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
22%|██▏ | 14/64 [03:04<12:10, 14.61s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (31, 270000) Accuracy 0.9647370370370371 Shape of flattened_features before texton: (27, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (27, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
23%|██▎ | 15/64 [03:19<12:01, 14.72s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (31, 270000) Accuracy 0.9646555555555556 Shape of flattened_features before texton: (30, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (30, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
25%|██▌ | 16/64 [03:34<11:50, 14.81s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (34, 270000) Accuracy 0.9665666666666667 Shape of flattened_features before texton: (9, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (9, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
27%|██▋ | 17/64 [03:48<11:26, 14.61s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (13, 270000) Accuracy 0.9641111111111111 Shape of flattened_features before texton: (12, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (12, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
28%|██▊ | 18/64 [04:02<11:05, 14.46s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (16, 270000) Accuracy 0.9774333333333334 Shape of flattened_features before texton: (12, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (12, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
30%|██▉ | 19/64 [04:17<10:46, 14.37s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (16, 270000) Accuracy 0.9695925925925926 Shape of flattened_features before texton: (15, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (15, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
31%|███▏ | 20/64 [04:31<10:32, 14.37s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (19, 270000) Accuracy 0.9763 Shape of flattened_features before texton: (18, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (18, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
33%|███▎ | 21/64 [04:45<10:18, 14.39s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (22, 270000) Accuracy 0.9696592592592592 Shape of flattened_features before texton: (21, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (21, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
34%|███▍ | 22/64 [05:00<10:08, 14.48s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (25, 270000) Accuracy 0.969362962962963 Shape of flattened_features before texton: (21, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (21, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
36%|███▌ | 23/64 [05:15<09:55, 14.52s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (25, 270000) Accuracy 0.9695111111111111 Shape of flattened_features before texton: (24, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (24, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
38%|███▊ | 24/64 [05:29<09:41, 14.54s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (28, 270000) Accuracy 0.9711555555555555 Shape of flattened_features before texton: (24, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (24, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
39%|███▉ | 25/64 [05:44<09:27, 14.55s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (28, 270000) Accuracy 0.9723888888888889 Shape of flattened_features before texton: (27, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (27, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
41%|████ | 26/64 [05:59<09:16, 14.63s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (31, 270000) Accuracy 0.9704037037037037 Shape of flattened_features before texton: (27, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (27, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
42%|████▏ | 27/64 [06:14<09:05, 14.75s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (31, 270000) Accuracy 0.9715666666666667 Shape of flattened_features before texton: (30, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (30, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
44%|████▍ | 28/64 [06:29<08:53, 14.82s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (34, 270000) Accuracy 0.9720962962962963 Shape of flattened_features before texton: (33, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (33, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
45%|████▌ | 29/64 [06:44<08:39, 14.85s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (37, 270000) Accuracy 0.9561777777777778 Shape of flattened_features before texton: (36, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (36, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
47%|████▋ | 30/64 [06:59<08:26, 14.91s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (40, 270000) Accuracy 0.9618185185185185 Shape of flattened_features before texton: (36, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (36, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
48%|████▊ | 31/64 [07:14<08:12, 14.92s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (40, 270000) Accuracy 0.9629925925925926 Shape of flattened_features before texton: (39, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (39, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
50%|█████ | 32/64 [07:29<08:01, 15.05s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (43, 270000) Accuracy 0.9673962962962963 Shape of flattened_features before texton: (9, 270000) Shape of flattened_features before texton: (9, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
52%|█████▏ | 33/64 [07:43<07:33, 14.63s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (13, 270000) Accuracy 0.7871481481481482 Shape of flattened_features before texton: (12, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (12, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
53%|█████▎ | 34/64 [07:57<07:12, 14.41s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (16, 270000) Accuracy 0.9857296296296296 Shape of flattened_features before texton: (12, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (12, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
55%|█████▍ | 35/64 [08:11<06:55, 14.31s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (16, 270000) Accuracy 0.9857296296296296 Shape of flattened_features before texton: (15, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (15, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
56%|█████▋ | 36/64 [08:25<06:40, 14.30s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (19, 270000) Accuracy 0.985025925925926 Shape of flattened_features before texton: (18, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (18, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
58%|█████▊ | 37/64 [08:39<06:27, 14.34s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (22, 270000) Accuracy 0.9639814814814814 Shape of flattened_features before texton: (21, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (21, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
59%|█████▉ | 38/64 [08:54<06:15, 14.43s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (25, 270000) Accuracy 0.9635111111111111 Shape of flattened_features before texton: (21, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (21, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
61%|██████ | 39/64 [09:09<06:03, 14.53s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (25, 270000) Accuracy 0.9634444444444444 Shape of flattened_features before texton: (24, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (24, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
62%|██████▎ | 40/64 [09:23<05:47, 14.49s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (28, 270000) Accuracy 0.9638888888888889 Shape of flattened_features before texton: (24, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (24, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
64%|██████▍ | 41/64 [09:38<05:34, 14.52s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (28, 270000) Accuracy 0.7895296296296296 Shape of flattened_features before texton: (27, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (27, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
66%|██████▌ | 42/64 [09:53<05:22, 14.68s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (31, 270000) Accuracy 0.9804259259259259 Shape of flattened_features before texton: (27, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (27, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
67%|██████▋ | 43/64 [10:08<05:10, 14.78s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (31, 270000) Accuracy 0.980462962962963 Shape of flattened_features before texton: (30, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (30, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
69%|██████▉ | 44/64 [10:23<04:56, 14.84s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (34, 270000) Accuracy 0.9790962962962962 Shape of flattened_features before texton: (33, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (33, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
70%|███████ | 45/64 [10:38<04:43, 14.92s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (37, 270000) Accuracy 0.9599444444444445 Shape of flattened_features before texton: (36, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (36, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
72%|███████▏ | 46/64 [10:53<04:29, 14.94s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (40, 270000) Accuracy 0.964062962962963 Shape of flattened_features before texton: (36, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (36, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
73%|███████▎ | 47/64 [11:08<04:15, 15.00s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (40, 270000) Accuracy 0.9635481481481482 Shape of flattened_features before texton: (39, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (39, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
75%|███████▌ | 48/64 [11:23<04:01, 15.10s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (43, 270000) Accuracy 0.9655814814814815 Shape of flattened_features before texton: (18, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (18, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
77%|███████▋ | 49/64 [11:38<03:43, 14.89s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (22, 270000) Accuracy 0.9745481481481482 Shape of flattened_features before texton: (21, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (21, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
78%|███████▊ | 50/64 [11:52<03:27, 14.82s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (25, 270000) Accuracy 0.9781592592592593 Shape of flattened_features before texton: (21, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (21, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
80%|███████▉ | 51/64 [12:07<03:11, 14.75s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (25, 270000) Accuracy 0.9785259259259259 Shape of flattened_features before texton: (24, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (24, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
81%|████████▏ | 52/64 [12:21<02:56, 14.68s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (28, 270000) Accuracy 0.9835074074074074 Shape of flattened_features before texton: (27, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (27, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
83%|████████▎ | 53/64 [12:36<02:41, 14.71s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (31, 270000) Accuracy 0.9657407407407408 Shape of flattened_features before texton: (30, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (30, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
84%|████████▍ | 54/64 [12:51<02:27, 14.74s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (34, 270000) Accuracy 0.9641222222222222 Shape of flattened_features before texton: (30, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (30, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
86%|████████▌ | 55/64 [13:06<02:13, 14.83s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (34, 270000) Accuracy 0.9640481481481481 Shape of flattened_features before texton: (33, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (33, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
88%|████████▊ | 56/64 [13:21<01:59, 14.95s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (37, 270000) Accuracy 0.9648629629629629 Shape of flattened_features before texton: (33, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (33, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
89%|████████▉ | 57/64 [13:36<01:45, 15.01s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (37, 270000) Accuracy 0.9735185185185186 Shape of flattened_features before texton: (36, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (36, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
91%|█████████ | 58/64 [13:52<01:30, 15.06s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (40, 270000) Accuracy 0.9756148148148148 Shape of flattened_features before texton: (36, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (36, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
92%|█████████▏| 59/64 [14:07<01:15, 15.05s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (40, 270000) Accuracy 0.9757 Shape of flattened_features before texton: (39, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (39, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
94%|█████████▍| 60/64 [14:22<01:00, 15.10s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (43, 270000) Accuracy 0.9767259259259259 Shape of flattened_features before texton: (42, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (42, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
95%|█████████▌| 61/64 [14:37<00:45, 15.09s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (46, 270000) Accuracy 0.963362962962963 Shape of flattened_features before texton: (45, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (45, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
97%|█████████▋| 62/64 [14:53<00:30, 15.26s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (49, 270000) Accuracy 0.9642037037037037 Shape of flattened_features before texton: (45, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (45, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
98%|█████████▊| 63/64 [15:08<00:15, 15.38s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (49, 270000) Accuracy 0.9661777777777778 Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering!
100%|██████████| 64/64 [15:24<00:00, 14.44s/it]
Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Accuracy 0.9646851851851852 18 32
In [ ]:
print(np.max(accuracies1))
print(np.max(accuracies2))
print(combinations[np.argmax(accuracies2)])
0.9790925925925926 0.9857296296296296 [1, 0, 0, 0, 0, 0]
Of all of the feature combinations the prewitt and lapacian features alongside the textons give the best results.
The MR 8 drops the accuracy about 0.06.
In [ ]:
In [ ]:
# Running different standard deviations
array = np.arange(5, 16)
accuracies = []
for x in array:
image = cv2.imread("Images/image-35.jpg")
mask = cv2.imread("Images/mask-35.png", cv2.IMREAD_GRAYSCALE)
mask = mask>=127.5
null = np.ones_like(mask)*255
classify_inst = Stat_Classifier(image)
original_features = classify_inst.getFeatures(image,null,False,MR8=False,desired_sigma=x**0.5)
perpixel_features = np.swapaxes(original_features,0,1)
log_reg = sklearn.linear_model.LogisticRegression().fit(perpixel_features,mask.flatten())
ver = cv2.imread("Images/image-110.jpg")
mask = cv2.imread("Images/mask-110.png", cv2.IMREAD_GRAYSCALE)
mask = mask>=127.5
ver_features = classify_inst.getFeatures(ver,null,False,MR8=False,texton=True,desired_sigma=x**0.5)
ver_perpixel_features = np.swapaxes(ver_features,0,1)
predictions = log_reg.predict(ver_perpixel_features)
accuracies.append(log_reg.score(ver_perpixel_features,mask.flatten()))
Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000) Shape of flattened_features before texton: (48, 270000)
c:\Users\Tumi\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Shape of flattened_features before texton: (48, 270000) Shape of flattened_features before texton: (48, 270000) (48, 270000) Clustering! Shape of filtered_textons: (4, 270000) Shape of concatenated_features: (52, 270000)
In [ ]:
print(accuracies)
[0.9632407407407407, 0.9643666666666667, 0.9666777777777777, 0.9642037037037037, 0.9641148148148149, 0.9646851851851852, 0.9656592592592592, 0.9670666666666666, 0.967374074074074, 0.9639666666666666, 0.9646888888888889]
Iterating the std of the gaussian,log and dog doesnt have too much of a noticible impact on the IOU score.